CN115460087A - Method and device for deploying business process in cloud computing environment - Google Patents
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Abstract
The disclosure provides a method and a device for deploying a business process in a cloud computing environment, and belongs to the technical field of communication. The method comprises the following steps: initializing and generating N strategies which meet physical constraint conditions and are used for deploying K tasks on a plurality of cloud resources; the following A1 to A4 were repeatedly performed W times: a1: dividing N strategies meeting physical constraint conditions into a first group and a second group based on the fitness; a2: preprocessing the cloud resources in each policy of the first set; a3: if Q strategies do not meet the physical constraint conditions, regenerating the Q strategies until Q strategies meeting the physical constraint conditions are obtained; a4: determining the fitness of each strategy in the strategies meeting the physical constraint conditions; and determining the strategy with the minimum value of the N fitness degrees calculated in the W time as a deployment strategy of the K tasks. Based on the technical scheme provided by the embodiment of the disclosure, appropriate cloud resources and service strategies can be selected for the business.
Description
Technical Field
The disclosure belongs to the technical field of communication, and particularly relates to a method and a device for deploying a business process in a cloud computing environment.
Background
With the development of virtualization technology and distributed computing technology, cloud computing has become a strategic focus of the development of the information industry, and can support the use of cost-effective computing resources.
Generally, a business process is composed of a plurality of tasks, and needs to be connected according to a certain logical relationship and a certain precedence relationship, and each connection edge is attached with a certain timing constraint. Delivery of user business process requirements has time constraints and needs to be performed on time in a logical order to avoid as much as possible a loss of quality of user experience due to a business violation.
However, how to select a suitable cloud resource and an optimal service policy for each task according to a business process sequence, and simultaneously satisfy a timing constraint in a business process is a technical problem which needs to be solved urgently at present.
Disclosure of Invention
The embodiment of the disclosure aims to provide a method and a device for deploying a business process in a cloud computing environment, which can solve the problem of selecting proper cloud resources and service strategies for businesses.
In order to solve the technical problem, the present disclosure is implemented as follows:
in a first aspect, an embodiment of the present disclosure provides a method for deploying a business process in a cloud computing environment, where the method includes: initializing and generating N strategies which meet physical constraint conditions and are used for deploying K tasks on a plurality of cloud resources, wherein the K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of storage capacity and computing capacity, and K and N are integers larger than 1; the following A1 to A4 were repeatedly performed W times: a1: dividing the N strategies meeting the physical constraint condition into a first group and a second group based on the fitness, wherein the fitness of the strategies of the first group is greater than that of the strategies of the second group, and the fitness indicates the deployment cost and the default cost degree of the strategies; a2: preprocessing the cloud resources of each strategy of the first group to obtain a preprocessed strategy, and determining whether the preprocessed strategy meets physical constraint conditions; a3: if Q strategies do not meet the physical constraint conditions, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint conditions or not until Q strategies meeting the physical constraint conditions are obtained, wherein Q is a positive integer; a4: determining the fitness of each strategy in the second group of strategies, the Q strategies meeting the physical constraint conditions and the strategies meeting the physical constraint conditions in the preprocessed strategies; and determining the strategy with the minimum median of the N fitness calculated in the W-th time as the deployment strategy of the K tasks, wherein W is an integer larger than 1.
In a second aspect, an embodiment of the present disclosure provides an apparatus for deploying a business process, where the apparatus includes: the device comprises a generating module, an executing module and a determining module; the generating module is used for initializing and generating N strategies which meet physical constraint conditions and are used for deploying K tasks on the plurality of cloud resources, wherein the K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of storage capacity and computing capacity, and K and N are integers which are larger than 1; an execution module for repeatedly executing the following A1 to A4 for W times: a1: dividing N strategies meeting physical constraint conditions into a first group and a second group based on the fitness, wherein the fitness of the strategies of the first group is greater than that of the strategies of the second group, and the fitness indicates the deployment cost and the default cost degree of the strategies; a2: preprocessing the cloud resources of each strategy of the first group to obtain a preprocessed strategy, and determining whether the preprocessed strategy meets physical constraint conditions; a3: if Q strategies do not meet the physical constraint conditions, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint conditions or not until Q strategies meeting the physical constraint conditions are obtained, wherein Q is a positive integer; a4: determining the fitness of each strategy in the second group of strategies, Q strategies meeting the physical constraint conditions and the strategies meeting the physical constraint conditions in the preprocessed strategies; the determining module is further used for determining the strategy with the minimum fitness median of the N calculated in the W time by the executing module as the deployment strategy of the K tasks; w is an integer greater than 1.
In a third aspect, the disclosed embodiments provide a server, which includes a processor, a memory, and a program or an instruction stored in the memory and executable on the processor, and when executed by the processor, the program or the instruction implements the steps of the method for business process deployment in a cloud computing environment according to the first aspect.
In a fourth aspect, the disclosed embodiments provide a readable storage medium, on which a program or instructions are stored, which when executed by a processor, implement the steps of the method for business process deployment in a cloud computing environment according to the first aspect.
In a fifth aspect, the present disclosure provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to execute a program or an instruction to implement the method for deploying a business process in a cloud computing environment according to the first aspect.
In a sixth aspect, the present disclosure provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the steps of the method for business process deployment in a cloud computing environment according to the first aspect.
In the embodiment of the present disclosure, first, N policies that are deployed on a plurality of cloud resources and satisfy physical constraints may be generated for K tasks in at least one business process, where the physical constraints include constraints of storage capacity and computing capacity, and then the following steps are repeatedly performed W times: dividing the N strategies into 2 groups according to the fitness, then preprocessing a group of strategies with a large fitness value, judging whether the preprocessed new strategies meet physical constraint conditions or not, continuously regenerating the strategies which are not met until the strategies in the group meet the physical constraint conditions, and then preprocessing the strategies based on the fitness grouping. And finally, after the W-th processing, determining the strategy with the minimum fitness value in the N strategies as the deployment strategy of each task in the business process. Based on the mode, the deployment strategy with low deployment cost meeting the task requirements can be comprehensively determined according to the time sequence requirements of each task in the business process, the computing capacity, the storage capacity and the deployment cost of the cloud resources and the default cost.
Drawings
Fig. 1 is a schematic diagram illustrating deployment of a business process on cloud resources in a cloud computing environment according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a method for business process deployment in a cloud computing environment according to an embodiment of the present disclosure;
fig. 3 is a second schematic flowchart of a method for deploying a business process in a cloud computing environment according to an embodiment of the present disclosure;
fig. 4 is a third schematic flowchart of a method for business process deployment in a cloud computing environment according to an embodiment of the present disclosure;
fig. 5 is one of possible structural diagrams of a device for business process deployment according to an embodiment of the present disclosure;
fig. 6 is a second schematic structural diagram of a possible apparatus for business process deployment according to an embodiment of the present disclosure;
fig. 7 is a hardware schematic diagram of a device for business process deployment according to an embodiment of the present disclosure.
Detailed Description
For ease of understanding, the relevant terms referred to in the embodiments of the present disclosure are explained first:
in a cloud computing environment, different cloud service resources (denoted as cloud resources) may be included, and a user in a network may make a functional requirement and select a different cloud service resource for renting, so as to fulfill a requirement of a network user. Generally, a user requirement is not a single atomic task, in the method for deploying the service flow in the cloud computing environment provided by the embodiment of the disclosure, the user requirement is split into a series of task sets with a logical relationship and a timing constraint, each task rents different cloud service resources, and a deployment strategy for minimizing the service cost is found according to different pricing strategies of the cloud service resources under the condition that the timing constraint of the service flow is satisfied as much as possible.
1. Business process
And representing the business process bp by a binary group, wherein bp = { Tsk, edg }, tsk represents a task set contained in the business process, and Tsk = { Tsk = 1 ,tsk 2 8230, edg represents the sequential edges between different tasks in the business process,
2. task
The task tsk is represented by a six-tuple, wherein tsk = { nm, dsc, χ, β, dur, pen }, nm represents the name of tsk, dsc represents the textual description of the task, χ represents the amount of computation required by the task, β represents the storage space required by the task, dur represents the duration of execution of the task, and pen represents the violation cost of the task due to the exceeding of the timing constraint.
3. Cloud resources
And a quadruplet is used for representing a cloud resource CR, wherein CR = { pvd, cmp, str, pcs }, the cloud service provider is represented by pvd, cmp represents the computing capacity of a server corresponding to the cloud resource, str represents the memory space of the cloud resource, and pcs represents the pricing strategy of the cloud resource.
In general, cloud service pricing policies can be divided into the following two modes:
(1) Prepaid policy m 1
Cloud servers with different configurations can be selected, and cloud resources can be used randomly within a certain time in a mode of paying a certain fee in advance.
(2) Charging strategy m according to minutes 2
I.e., a buy-and-use manner, the cloud resources are paid for by unit price and usage time.
The technical solutions in the embodiments of the present disclosure will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present disclosure, and it is to be understood that the described embodiments are only some embodiments, but not all embodiments, of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without inventive step, are intended to be within the scope of the present disclosure.
The terms first, second and the like in the description and in the claims of the present disclosure are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the disclosure may be practiced other than those illustrated or described herein, and that the words "first", "second", etc. are generally one, and do not necessarily limit the number of words of the word, e.g., the first word may be one, or may be multiple. In addition, "and/or" in the specification and claims means at least one of connected objects, a character "/", and generally means that the former and latter related objects are in an "or" relationship.
It is noted that the techniques described in the embodiments of the present disclosure are not limited to LTE (Long Term Evolution)/LTE-a (LTE-Advanced) systems, but may also be used in other wireless communication systems, such as CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), FDMA (Frequency Division Multiple Access), OFDMA (Orthogonal Frequency Division Multiple Access), SC-FDMA (Single-carrier Frequency-Division Multiple Access), and other systems. The terms "system" and "network" are often used interchangeably in embodiments of the present application, and the described techniques may be used for both the above-mentioned systems and radio technologies, as well as for other systems and radio technologies. However, the following description describes the NR system for purposes of example, and NR terminology is used in much of the description below, although the techniques may also be applied to applications other than NR system applications, such as 6G (6 th Generation ) communication systems.
The method for deploying the business process in the cloud computing environment provided by the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings through a specific embodiment and an application scenario thereof.
Fig. 1 is a schematic diagram of deployment of a business process on a cloud resource in a cloud computing environment according to an embodiment of the present disclosure. As shown in fig. 1, i cloud resources are included, i being an integer greater than 1, each cloud resource including: cloud provider, computing power, storage space, and pricing policy, as shown in fig. 1, respectively. Take the deployment of 2 business processes as an example, wherein, the business process bp 1 The method comprises 4 tasks which are respectively as follows: tsk (r) tsk 1-1 、tsk 1-2 、tsk 1-3 And tsk 1-4 . Business process bp 2 Comprises 3 tasks, which are respectively as follows: tsk 2-1 、tsk 2-2 And tsk 2-3 。
Wherein tsk is executed 1-1 Has a duration interval of [3,4 ]]Execute tsk 1-2 Has a duration interval of [1,3 ]]Execute tsk 1-3 Has a duration interval of [2,4 ]]Execute tsk 1-4 Has a duration interval of [3,5 ]]Execute tsk 2-1 Has a duration interval of [1,2 ]]Execute tsk 2-2 Has a duration interval of [2,4 ]]Execute tsk 2-3 Has a duration interval of [4,6 ]]。
Wherein tsk 1-1 And tsk 1-2 Deployed in CR 1 Go to, tsk 1-3 、tsk 1-4 And tsk 2-3 Deployed in CR 2 Go to, tsk 2-1 And tsk 2-2 Is deployed in CR i The logical order of execution of the tasks is the logical order indicated by the arrows between the tasks in fig. 1.
Fig. 2 is a flowchart illustrating a method for deploying a business process in a cloud computing environment according to an embodiment of the present disclosure, as shown in fig. 2, the method includes the following steps S201 to S203:
s201, initializing and generating N strategies which are used for deploying K tasks on a plurality of cloud resources and meet physical constraint conditions.
The K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of storage capacity and calculation capacity, and both K and N are positive integers larger than 1.
Alternatively, in conjunction with fig. 2, as shown in fig. 3, the above S201 may be specifically performed by the following S21 and S22:
s21, generating N strategies for deploying K tasks on the plurality of cloud resources, and determining whether each strategy meets physical constraint conditions.
Illustratively, at initialization, N policies may be randomly generated.
It should be noted that, if any one of the storage capacity and the computing capacity in one policy does not satisfy the corresponding constraint condition, the policy does not satisfy the physical constraint condition, and if both the storage capacity and the computing capacity in one policy satisfy the corresponding constraint condition, the policy satisfies the physical constraint condition.
For example, to facilitate understanding of the technical solutions provided by the embodiments of the present disclosure, in table 1, an exemplary table of a policy for task deployment of a business process provided by the embodiments of the present disclosure is provided, assuming that 3 cloud resources can provide services for 7 tasks of the business process 1 and the business process 2, where 20 deployed policies are randomly generated, it may be determined whether each policy of the 20 policies meets a physical constraint condition.
For the sake of description, the policy 1 is "1323113", and the policy 1 indicates that the task 1 is deployed on the cloud resource 1, the task 2 is deployed on the cloud resource 3, the task 3 is deployed on the cloud resource 2, the task 4 is deployed on the cloud resource 3, the task 5 is deployed on the cloud resource 1, the task 6 is deployed on the cloud resource 1, and the task 7 is deployed on the task 3.
TABLE 1
| Task 1 | Task 2 | Task 3 | Task 4 | Task 5 | Task 6 | Task 7 | |
| Strategy 1 | 1 | 3 | 2 | 3 | 1 | 1 | 3 |
| Strategy 2 | 2 | 3 | 1 | 2 | 3 | 2 | 1 |
| Strategy 3 | 1 | 2 | 3 | 1 | 2 | 3 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| Strategy 20 | 3 | 2 | 1 | 2 | 3 | 2 | 3 |
S22, if the P strategies do not meet the physical constraint conditions, the P strategies are regenerated, and whether the regenerated strategies meet the physical constraint conditions or not is determined until N strategies meeting the physical constraint conditions are obtained.
Wherein P is a positive integer less than or equal to N.
Specifically, if there are P policies that do not satisfy the physical constraint condition among the N policies, the P policies are deleted, the P policies are regenerated, and it is determined whether the regenerated P policies satisfy the physical constraint condition, if all of the regenerated P policies satisfy the physical constraint condition, S202 described below is executed, and if there is a policy that does not satisfy the physical constraint condition among the regenerated P policies, the new policies are continuously deleted and generated until N policies that satisfy the physical constraint condition are obtained, and S202 described below is executed.
Exemplarily, assuming that 5 policies out of the 20 policies do not satisfy the physical constraint condition, the 5 policies may be deleted and the 5 policies are regenerated, and then it is determined whether the 5 policies satisfy the physical constraint condition, if all the 5 policies satisfy the physical constraint condition, the following S202 is continuously executed, and if at least one policy out of the 5 policies that are regenerated does not satisfy the physical constraint condition, the policy that is not satisfied is continuously deleted and the physical constraint condition is regenerated and continuously determined whether the policy satisfies the physical constraint condition.
S202, repeatedly executing the following A1 to A4 for W times:
a1: the N strategies meeting the physical constraint conditions are divided into a first group and a second group based on the fitness.
And the fitness of the strategy of the first group is greater than that of the strategy of the second group.
In the embodiment of the disclosure, the fitness indicates the deployment cost and the default cost degree of the policy. The smaller the fitness is, the lower the cost of indicating strategy deployment is, and the better the strategy is; the greater the fitness, the higher the cost of indicating policy deployment and the worse the policy.
Illustratively, the first group of strategies can be N/2 strategies of N strategies with the fitness degree ranked from small to large by N/2, and the second group of strategies can be N/2 strategies of N strategies with the fitness degree ranked from small to large by N/2. Alternatively, the first group of policies may be policies whose fitness among the N policies is greater than or equal to the preset fitness, and the second group of policies may be policies whose fitness among the N policies is less than the preset fitness.
Continuing with the above example, after going through the processing of S202, 20 policies that satisfy the physical constraint condition may be obtained, and then the fitness of each policy may be calculated, thereby obtaining the fitness of 20 policies. By taking the first grouping method as an example, 10 first group policies with relatively high fitness and 10 second group policies with relatively low fitness can be obtained.
A2: and preprocessing the cloud resources of each strategy in the first group to obtain a preprocessed strategy, and determining whether the preprocessed strategy meets physical constraint conditions.
With continued reference to the above example, for the 10 policies with the first group having relatively high fitness, preprocessing the cloud resources corresponding to the task in each policy to obtain new 10 policies, and then calculating whether the 10 new policies satisfy the physical constraint condition.
It should be noted that, if the pre-processed strategies all meet the physical constraint condition, the fitness of the pre-processed strategies is directly determined, and the N strategies are grouped again based on the fitness of the strategies in the second group and the fitness of the pre-processed strategies, and the next processing is executed.
A3: and if Q strategies do not meet the physical constraint conditions, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint conditions or not until Q strategies meeting the physical constraint conditions are obtained.
Wherein Q is a positive integer.
A4: and determining the fitness of each strategy in the second group of strategies, the Q strategies meeting the physical constraint conditions and the strategies meeting the physical constraint conditions in the preprocessed strategies.
It should be noted that, in A4, N strategies are obtained in total.
The fitness of the policy of the second group determined in A4 may be the fitness calculated in the grouping in A1 each time A1 to A4 are executed. That is, the fitness required to be calculated in A4 is the fitness of the policy processed by A2 and A3 for the policy of the first group.
And S203, determining the strategy with the minimum value of the N fitness degrees determined at the W time as a deployment strategy of the K tasks.
Wherein W is a positive integer.
It will be appreciated that a minimum fitness value may indicate that the overall cost of deployment of the policy is minimal.
The embodiment of the disclosure provides a method for deploying business processes in a cloud computing environment, which includes the steps that firstly, N strategies meeting physical constraint conditions and deployed on a plurality of cloud resources can be initialized and generated for K tasks in at least one business process, and the physical constraint conditions include constraint conditions of storage capacity and computing capacity; then, the following steps are repeatedly performed W times: dividing the N strategies into 2 groups according to the fitness, then preprocessing a group of strategies with a large fitness value, judging whether the preprocessed new strategies meet physical constraint conditions or not, continuously regenerating the preprocessed new strategies if the preprocessed new strategies do not meet the physical constraint conditions until the strategies in the group all meet the physical constraint conditions, and then preprocessing the strategies based on the fitness grouping. And finally, after the W-th processing, determining the strategy with the minimum fitness value in the N strategies as the deployment strategy of each task in the business process. Based on the mode, the deployment strategy with low deployment cost meeting the task requirements can be comprehensively determined according to the time sequence requirements of each task in the business process, the computing capacity, the storage capacity and the deployment cost of the cloud resources and the default cost.
Optionally, in the method for deploying a business process in a cloud computing environment provided by the embodiment of the present disclosure, "determining whether each policy satisfies a physical constraint condition" in S21 may be performed by the following S11:
s11, determining whether the storage capacity and the computing capacity of the cloud resources in each strategy meet corresponding constraint conditions.
In combination with policy 1 in table 1, it needs to calculate whether the storage capacity and the computing capacity of each cloud resource satisfy the corresponding constraint condition when executing the corresponding task. For example, it is required whether the storage capacity and the computing capacity of the computing cloud resource 1 satisfy the corresponding constraint conditions when executing the task 1, the task 5, and the task 6, whether the storage capacity and the computing capacity of the cloud resource 2 satisfy the corresponding constraint conditions when executing the task 3, and whether the storage capacity and the computing capacity of the cloud resource 3 satisfy the corresponding constraint conditions when executing the task 2, the task 4, and the task 7.
Based on the scheme, after the strategies of each task, which are deployed on the cloud resource, are generated, whether the storage capacity of the cloud resource related to each strategy meets the requirements of the tasks deployed on the resource and whether the computing capacity meets the requirements of the tasks deployed on the resource can be judged, so that the strategies which do not meet the physical constraint condition in the randomly generated strategies can be eliminated.
Optionally, in the method for deploying a business process in a cloud computing environment provided by the embodiment of the present disclosure, when determining whether the storage capacity of the cloud resource in each policy satisfies the corresponding constraint condition, the S11 may include the following S11a:
s11a, determining whether the storage capacity of the cloud resources in each strategy meets the corresponding constraint condition based on the storage space required by the task, the storage space of the cloud resources and the decision condition that the task is deployed in the cloud resources.
For example, whether the storage capacity of the cloud resource meets the storage constraint condition under the policy may be determined based on the following formula (1).
Wherein, X ij Representing tasks tsk i Deployed in cloud resource CR j Decision case of (1), X ij =1 denotes task tsk i Deployed in cloud resource CR j Upper, X ij =0 for task tsk i Not deployed in cloud resource CR j Upper, CR j Str denotes cloud resource CR j The storage space of (2).
It can be understood that if the storage capacity of the cloud resource in one policy satisfies the above formula (1), the storage capacity of the cloud resource in the policy satisfies the corresponding constraint condition, and if the storage capacity of the cloud resource in one policy does not satisfy the above formula (1), the policy does not satisfy the physical constraint condition.
Based on the scheme, whether the storage capacity of each cloud resource related in the strategy meets the requirement of the task or not can be determined based on the storage space required by each task in the business process, the deployment condition of each task on the cloud resource in one strategy and the storage space of each cloud resource, so that whether the strategy is a strategy meeting the storage requirement or not can be determined, and subsequent screening can be continued conveniently.
Optionally, in the method for deploying a business process in a cloud computing environment provided by the embodiment of the present disclosure, when determining whether the storage capacity of the cloud resource in each policy satisfies the corresponding constraint condition, the S11 may include the following S11b:
s11b, determining whether the computing capacity of the cloud resources in each strategy meets corresponding constraint conditions based on the computing amount required by the tasks, the computing capacity of the cloud resources and the decision condition that the tasks are deployed in the cloud resources.
For example, whether the storage capacity of the cloud resource satisfies the storage constraint condition under each policy may be determined based on the following formula (1).
Wherein, CR j Cmp represents a cloud resource CR j The computing power of (a).
It can be understood that if the computing capability of the cloud resource in one policy satisfies the above formula (2), the computing capability of the cloud resource in the policy satisfies the corresponding constraint condition, and if the computing capability of the cloud resource in one policy does not satisfy the above formula (2), the policy does not satisfy the physical constraint condition.
Based on the scheme, whether the computing capacity of each cloud resource related in the strategy meets the requirement of the task or not can be determined based on the computing capacity required by each task in the business process, the deployment condition of each task on the cloud resource in one strategy and the computing capacity of each cloud resource, so that whether the strategy is a strategy meeting the computing requirement or not can be determined, and the subsequent screening can be continued conveniently.
Optionally, in the method for deploying the service flow in the cloud computing environment provided by the embodiment of the present disclosure, the preprocessing of the cloud resource in the A2 may be at least one of the following two manners:
pretreatment method 1: and exchanging cloud resources of at least one task in every two strategies of the first group to obtain the exchanged strategies.
Illustratively, the policy "1231232 "and policy" 321232Exchanging the cloud resources from task 4 to task 6 in step 3 "to obtain 2 new policies" 1232322 "and" 3211233”。
Pretreatment method 2: and randomly replacing the cloud resource of at least one task in each strategy of the first group to obtain a replaced strategy.
Illustratively, the policy "1232322 "task 3's cloud resources are randomly changed, e.g., to a new policy" 1212322”。
For example, if the cloud resource preprocessing includes the above 2 manners, the preprocessing manner 1 may be executed first, and then the preprocessing manner 2 is executed, or the preprocessing manner 2 may be executed first, and then the preprocessing manner 1 is executed, which is not limited in this disclosure.
It is appreciated that in the disclosed embodiment, the policy obtained after the preprocessing is actually a completely new deployment policy, and therefore, it is necessary to continue to determine whether the physical constraint condition is satisfied for the new policy.
Based on the scheme, after N strategies are grouped based on the fitness, the strategies with higher deployment cost and default cost indicated by the fitness can be preprocessed, so that a group of strategies is newly generated based on the group of strategies, the subsequent screening of the strategies is facilitated, and the strategy with the optimal fitness is selected for task deployment.
Optionally, in the method for deploying the business process in the cloud computing environment provided by the embodiment of the present disclosure, before the foregoing A1, the following A5 to A7 are further included:
a5: the deployment cost for each policy is calculated.
A6: penalty costs for each policy are calculated.
A7: and determining the fitness of each strategy according to the deployment cost and the default cost.
For example, the fitness of a policy = deployment cost + penalty cost.
Based on the scheme, before executing A1, the fitness of the N policies meeting the physical constraint condition generated in S201 may be determined first to evaluate the deployment cost and the degree of the default cost of each policy, so as to facilitate subsequent continuous preprocessing of the policies with higher deployment cost and default cost, and subsequent processing is performed based on the policies obtained again after preprocessing, so as to obtain the policy with the optimal fitness.
Optionally, in the method for deploying the business process in the cloud computing environment provided by the embodiment of the present disclosure, the A5 may be specifically executed by the following a51:
a51: and calculating the deployment cost of each strategy based on the maximum execution time of the task, the unit time price of the cloud resource pricing and the decision condition of the target pricing strategy in the cloud resource selected by the task.
Illustratively, the deployment cost for each policy may be determined based on equation (3) below.
Wherein，Max(tsk i Dur) represents the task tsk i Maximum execution time of c ij Representing tasks tsk i Selected cloud resource CR j Price per unit time, X, of the pricing ijm Representing tasks tsk i Selecting cloud resource CR j The decision condition of the pricing strategy m can be a binary variable. For example, X ijm =1 denotes task tsk i Selecting cloud resource CR j Pricing strategy m, X of ijm =0 task tsk i Unselected cloud resource CR j The pricing policy m.
Based on the scheme, the deployment cost of each strategy can be calculated based on the maximum execution time of each task demand, the pricing strategy of each cloud resource, the unit time price of cloud resource pricing and the decision condition of the target pricing strategy selected by the task, so that a better deployment strategy can be comprehensively screened out based on the deployment cost.
Optionally, in the method for deploying the business process in the cloud computing environment provided by the embodiment of the present disclosure, the A6 may be specifically executed by the following a61:
a61: and calculating the violation cost of each strategy based on the violation cost caused by violation of the time sequence constraint during the operation of the task and the decision condition of whether the task violates.
Illustratively, the violation cost for each policy may be determined based on equation (4) below.
Therein pen i Indicating due to task tsk i Violation cost, Y, caused by violation of timing constraints during deployment operations i Representing tasks tsk i Decision on whether to violate rules, Y i =1 indicates that task tsk is not fully met i Timing constraint of (2), Y i =0 indicating full compliance with task tsk i Does not cause a violation.
Further, the fitness of each of the above strategies may be determined based on the following formula (5).
FIT = COST + PEN equation (5)
Exemplarily, in S204 described above, the minimum fitness among the fitness of the N policies may be determined based on the following formula (5).
Objt = Min (COST + PEN) formula (6)
Based on the scheme, the violation cost of each strategy can be determined according to the cost caused by the fact that the task to be deployed violates the time sequence constraint in the operation and the condition that whether each task in the strategy is deployed on each cloud resource violates the time sequence or not, so that whether each strategy to be deployed meets the time sequence requirement of the task or not can be accurately judged, and the optimal deployment strategy meeting the requirement of each task can be accurately determined based on the time sequence subsequently.
Optionally, in the method for deploying a business process in a cloud computing environment provided by the embodiment of the present disclosure, in the foregoing a61, it may be determined whether the task violates the timing constraint based on the end time of the task, the start time of the task, and the target timing constraint corresponding to the task.
Illustratively, whether a task in a policy violates a timing constraint may be determined based on equation (6) below.
0≤F(tsk i )-S(tsk i )≤bp.stk i TC equation (7)
Wherein, F (tsk) i ) Representing tasks in a policy tsk i End time of, S (tsk) i ) Representing tasks in a policy tsk i Start time of (1), bp i TC represents the task tsk in the business process bp i The corresponding timing constraints.
Based on the scheme, whether the actual execution of the tasks in the strategy violates the timing constraints of the task requirements can be determined according to the processing time expected to be executed by each task in each strategy and the timing constraints of the task requirements in the business process, so that the deployment strategy meeting the task requirements can be determined based on the timing requirements and the estimated timing conditions of deployment.
Example (c):
fig. 4 is a schematic diagram of a business process optimization deployment process in a cloud computing environment based on a genetic algorithm, as shown in fig. 4, the above formula (6) may be used as an objective function of the genetic algorithm, that is, a business deployment strategy with the minimum fitness is found, a cloud resource location deployed by each task is recorded as a gene, all task deployment locations in the whole business process are used as a chromosome, the population size is set to 20, and the objective function of the formula (6) is used as an adaptive function to optimize the business process in the cloud computing environment.
S401, initializing a population, and assuming that 7 tasks in 2 business processes respectively deploy cloud resources at random, wherein the number example of the cloud resources corresponding to the deployment of the chromosomes is as follows: 1323113, 2312321, 1231231232,. And 3212323 (20 chromosomes in total). And (2) respectively judging whether the cloud resources selected in the strategy meet physical service constraint conditions, including storage constraint and calculation constraint, according to each deployment strategy according to the formulas (1) and (2), eliminating chromosomes which cannot be met, re-initializing to generate new chromosomes, and repeatedly judging whether the physical constraint conditions are met.
S402, for all individuals in the initial population, calculating deployment costs related to strategies in each chromosome according to a formula (3), judging whether the deployment strategies corresponding to each chromosome violate time sequence constraints of services according to a formula (4), and calculating default costs of each chromosome. And (3) generating an objective function of a formula (5) based on the formula (3) and the formula (4), calculating fitness function values of all individuals in the initial population by taking the objective function as a population fitness function, and substituting strategies represented by different chromosomes into the fitness function to calculate to obtain the fitness function values.
In this setting, the smaller the fitness function value is, the better the deployment strategy is.
And S403, judging whether iteration is finished.
S404, carrying out selection operation, and directly transmitting the optimized individuals in the population to the next generation population, wherein the fitness function value corresponding to the chromosome 2312321 is small, which shows that the deployment strategy is excellent, and the optimized individuals can be directly transmitted to the next generation population; performing cross operation, sequencing other individuals except the optimized individual in the population according to fitness, exchanging partial genes of adjacent individuals, and evolving the individuals in the population; and (4) carrying out mutation operation on the deployment strategy obtained by evolution, randomly changing partial gene values in individuals in the population, and then evolving the individuals in the population.
For example, the 4 th to 6th genes in chromosomes 1231232 and 3212323 are exchanged, resulting in 1232322 and 3211233. And aiming at each deployment strategy after exchange, respectively judging whether the exchanged cloud resources meet service physical constraint conditions including storage constraint and calculation constraint according to formulas (1) and (2), eliminating chromosomes which cannot be met, and performing initialization generation again.
For example, the 3 rd gene mutation in chromosome 1232322 is 1212322. And aiming at each deployment strategy after mutation, respectively judging whether the cloud resources after mutation operation meet service physical constraint conditions including storage constraint and calculation constraint according to formulas (1) and (2), eliminating chromosomes which cannot be met, and performing reinitialization generation.
And S406, obtaining the next generation population after the first evolution, evaluating the population by using a fitness function, and continuing to perform the operations from S402 to S405 until the maximum iteration number is met.
And S407, obtaining an optimized deployment strategy of the service process under the cloud computing environment according to the individual deployment mode with the minimum fitness function value in the last generation of population.
For example, if the chromosome with the smallest fitness function value is finally selected as 1122323, it means that: bp of bp 1 Tsk of 1 Deployed in CR 1 ，bp 1 Tsk of 2 Deployed in CR 1 ，bp 1 Tsk of 3 Deployed in CR 2 ，bp 1 Tsk of 4 Is deployed in CR 2 ，bp 2 Tsk of 1 Is deployed in CR 3 ，bp 2 Tsk of 2 Is deployed in CR 2 ，bp 2 Tsk of 3 Is deployed in CR 3 。
It should be noted that, in the method for deploying a business process in a cloud computing environment provided by the embodiment of the present disclosure, the execution subject may also be a device for deploying a business process, or a control module in the device for deploying a business process, which is used for executing the method for deploying a business process in a cloud computing environment. In the embodiment of the present disclosure, a method for a device for deploying a business process to execute the business process deployment in a cloud computing environment is taken as an example, and the device for deploying the business process provided in the embodiment of the present disclosure is described.
Fig. 5 is a schematic structural diagram of a device for business process deployment provided in an embodiment of the present disclosure, and as shown in fig. 5, the device 500 for business process deployment includes: a generating module 501, an executing module 502 and a determining module 503; a generating module 501, configured to generate N policies that satisfy physical constraints for deploying K tasks on a plurality of cloud resources, where the K tasks are tasks in at least one business process, the physical constraints include constraints of storage capacity and computing capacity, and K and N are integers greater than 1; an executing module 502, configured to repeatedly execute W times A1 to A4: a1: dividing N strategies meeting physical constraint conditions into a first group and a second group based on the fitness, wherein the fitness of the strategies of the first group is greater than that of the strategies of the second group, and the fitness indicates the deployment cost and the default cost degree of the strategies; a2: preprocessing the cloud resources of each strategy of the first group to obtain a preprocessed strategy, and determining whether the preprocessed strategy meets physical constraint conditions; a3: if Q strategies do not meet the physical constraint conditions, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint conditions or not until Q strategies meeting the physical constraint conditions are obtained, wherein Q is a positive integer; a4: determining the fitness of the strategies of the second group, Q strategies meeting the physical constraint conditions and each strategy in the pretreated strategies meeting the physical constraint conditions; a determining module 503, configured to determine a policy with a minimum median of the N fitness values calculated in the W-th time as a deployment policy of the K tasks, where W is an integer greater than 1.
Optionally, the generating module is specifically configured to: generating N strategies for deploying K tasks on a plurality of cloud resources, and determining whether each strategy meets a physical constraint condition; if P strategies do not meet the physical constraint condition, the P strategies are regenerated, whether the regenerated strategies meet the physical constraint condition is determined, and N strategies meeting the physical constraint condition are obtained until P is a positive integer smaller than or equal to N.
Optionally, the physical constraints include constraints on storage capacity and computing capacity; the determination module is specifically configured to: and determining whether the storage capacity and the computing capacity of the cloud resources in each strategy both meet corresponding constraint conditions.
Optionally, the execution module is specifically configured to: and determining whether the storage capacity of the cloud resources in each strategy meets the corresponding constraint condition or not based on the storage space required by the task, the storage space of the cloud resources and the decision condition that the task is deployed in the cloud resources.
Optionally, the execution module is specifically configured to: and determining whether the computing capacity of the cloud resources in each strategy meets the corresponding constraint condition or not based on the computing capacity required by the task, the computing capacity of the cloud resources and the decision condition that the task is deployed in the cloud resources.
Optionally, the execution module is specifically configured to: performing at least one of: exchanging cloud resources of at least one task in every two strategies of the first group to obtain an exchanged strategy; and randomly replacing the cloud resource of at least one task in each strategy of the first group to obtain a replaced strategy.
Optionally, the executing module is further configured to calculate a deployment cost of each policy before the fitness divides the N policies that satisfy the physical constraint condition into the first group and the second group; calculating the default cost of each strategy; and determining the fitness of each strategy according to the deployment cost and the default cost.
Optionally, the execution module is specifically configured to: and calculating the deployment cost of each strategy based on the maximum execution time of the task, the unit time price of cloud resource pricing and the decision condition of a target pricing strategy in the cloud resources selected by the task.
Optionally, the execution module is specifically configured to: and calculating the violation cost of each strategy based on the violation cost caused by violation of the time sequence constraint during the operation of the task and the decision condition of whether the task violates.
Optionally, the execution module is further configured to determine whether the task violates the timing constraint based on the end time of the task, the start time of the task, and a target timing constraint corresponding to the task.
The embodiment of the disclosure provides a device for business process deployment, which first of all, may generate N strategies meeting physical constraints deployed on a plurality of cloud resources for a total of K tasks in at least one business process, where the physical constraints include constraints of storage capacity and computing capacity; then, the following steps are repeatedly performed W times: dividing the N strategies into 2 groups according to the fitness, then preprocessing a group of strategies with a large fitness value, judging whether the preprocessed new strategies meet physical constraint conditions or not, continuously regenerating the strategies which are not met until the strategies in the group meet the physical constraint conditions, and then preprocessing the strategies based on the fitness grouping. And finally, after the W-th processing, determining the strategy with the minimum fitness value in the N strategies as the deployment strategy of each task in the business process. Based on the mode, the deployment strategy with low deployment cost for meeting the task requirements can be comprehensively determined according to the time sequence requirements of each task in the business process, the computing capacity, the storage capacity and the deployment cost of the cloud resources and the default cost.
The apparatus 500 for service flow deployment provided in the embodiment of the present disclosure can implement each process implemented in the method embodiments of fig. 1 to fig. 4, and is not described herein again to avoid repetition.
Optionally, as shown in fig. 6, an apparatus 600 for business process deployment is further provided in the embodiment of the present disclosure, which includes a processor 601, a memory 602, and a program or an instruction stored in the memory 602 and capable of running on the processor 601, where the program or the instruction is executed by the processor 601 to implement each process of the embodiment of the business process deployment method in the cloud computing environment, and can achieve the same technical effect, and is not described herein again to avoid repetition.
It should be noted that the network entity or the electronic device 700 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments of the present disclosure.
As shown in fig. 7, the electronic apparatus 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a ROM (Read Only Memory) 702 or a program loaded from a storage section 708 into a RAM (Random Access Memory) 703. In the RAM 703, various programs and data necessary for system operation are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An I/O (Input/Output) interface 705 is also connected to the bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a CRT (Cathode Ray Tube), an LCD (Liquid Crystal Display), and the like, a speaker, and the like; a storage section 708 including a hard disk and the like; and a communication section 709 including a Network interface card such as a LAN (Local Area Network) card, a modem, and the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, the processes described below with reference to the flowcharts may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. When the computer program is executed by the central processing unit (CPU 701), various functions defined in the system of the present application are executed.
The embodiment of the present disclosure further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the method embodiment for deploying the business process in the cloud computing environment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a ROM, a RAM, a magnetic or optical disk, and the like.
The embodiment of the present disclosure further provides a chip, where the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to execute a program or an instruction to implement each process of the method embodiment for service flow deployment in a cloud computing environment, and the same technical effect can be achieved, and in order to avoid repetition, details are not repeated here.
It should be understood that the chips mentioned in the embodiments of the present disclosure may also be referred to as system-on-chip, system-on-chip or system-on-chip, etc.
The embodiments of the present disclosure provide a computer program product including instructions, which when run on a computer, enables the computer to execute the steps of the method for deploying a business process in a cloud computing environment, and can achieve the same technical effects, and in order to avoid repetition, the steps are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a component of' 8230; \8230;" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element. Further, it is noted that the scope of the methods and apparatus in the embodiments of the present disclosure is not limited to performing functions in the order shown or discussed, but may include performing functions in a substantially simultaneous manner or in a reverse order based on the functions involved, e.g., the methods described may be performed in an order different than that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present disclosure.
While the present disclosure has been described with reference to the embodiments illustrated in the drawings, which are intended to be illustrative rather than restrictive, it will be apparent to those of ordinary skill in the art in light of the present disclosure that many more modifications may be made without departing from the spirit of the disclosure and the scope of the appended claims.
Claims (10)
1. A method for business process deployment in a cloud computing environment, the method comprising:
initializing and generating N strategies which meet physical constraint conditions and are used for deploying K tasks on a plurality of cloud resources, wherein the K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of storage capacity and computing capacity, and K and N are integers larger than 1;
the following A1 to A4 were repeatedly performed W times:
a1: dividing the N strategies meeting the physical constraint condition into a first group and a second group based on the fitness, wherein the fitness of the strategies of the first group is greater than that of the strategies of the second group, and the fitness indicates the deployment cost and the default cost degree of the strategies;
a2: preprocessing the cloud resources of each strategy of the first group to obtain a preprocessed strategy, and determining whether the preprocessed strategy meets the physical constraint condition;
a3: if Q strategies do not meet the physical constraint condition, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint condition or not until Q strategies meeting the physical constraint condition are obtained, wherein Q is a positive integer;
a4: determining fitness of each strategy in the second group of strategies, the Q strategies meeting the physical constraint conditions and the strategies meeting the physical constraint conditions in the preprocessed strategies;
and determining the strategy with the minimum fitness median of the N calculated in the W time as the deployment strategy of the K tasks, wherein W is an integer larger than 1.
2. The method of claim 1, wherein the initializing generates N policies that satisfy physical constraints for deploying the K tasks on the plurality of cloud resources, comprising:
generating N strategies for deploying K tasks on a plurality of cloud resources, and determining whether each strategy meets a physical constraint condition;
if P strategies do not meet the physical constraint condition, the P strategies are regenerated, whether the regenerated strategies meet the physical constraint condition is determined, and N strategies meeting the physical constraint condition are obtained until P is a positive integer smaller than or equal to N.
3. The method of claim 1, wherein the physical constraints include constraints on storage capacity and computational capacity;
the determining whether each policy satisfies the physical constraint condition comprises:
and determining whether the storage capacity and the computing capacity of the cloud resources in each strategy both meet corresponding constraint conditions.
4. The method of claim 3, wherein determining whether the storage capacity of the cloud resource in each policy satisfies the corresponding constraint comprises:
and determining whether the storage capacity of the cloud resources in each strategy meets the corresponding constraint condition or not based on the storage space required by the task, the storage space of the cloud resources and the decision condition that the task is deployed in the cloud resources.
5. The method of claim 3, wherein determining whether the computing power of the cloud resource in each policy satisfies the corresponding constraint comprises:
and determining whether the computing capacity of the cloud resources in each strategy meets the corresponding constraint condition or not based on the computing capacity required by the task, the computing capacity of the cloud resources and the decision condition that the task is deployed in the cloud resources.
6. The method of claim 1, wherein the pre-processing of the cloud resources for each policy of the first set to obtain a pre-processed policy comprises at least one of:
exchanging cloud resources of at least one task in every two strategies of the first group to obtain an exchanged strategy;
and randomly replacing the cloud resource of at least one task in each strategy of the first group to obtain a replaced strategy.
7. The method of claim 1, wherein before the fitness-based grouping of the N policies that satisfy the physical constraints into a first group and a second group, the method further comprises:
calculating the deployment cost of each strategy;
calculating the default cost of each strategy;
and determining the fitness of each strategy according to the deployment cost and the default cost.
8. The method of claim 7, wherein calculating the deployment cost for each policy comprises:
and calculating the deployment cost of each strategy based on the maximum execution time of the task, the unit time price of cloud resource pricing and the decision condition of a target pricing strategy in the cloud resources selected by the task.
9. The method of claim 7, wherein calculating the violation cost for each policy comprises:
and calculating the violation cost of each strategy based on the violation cost caused by the violation of the time sequence constraint during the operation of the task and the decision condition of whether the task violates.
10. An apparatus for business process deployment, wherein the apparatus for business process deployment comprises: the device comprises a generating module, an executing module and a determining module;
the generation module is used for generating N strategies which meet physical constraint conditions and are used for deploying K tasks on a plurality of cloud resources, wherein the K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of storage capacity and computing capacity, and K and N are integers which are larger than 1;
the execution module is used for repeatedly executing the following A1 to A4 for W times: a1: dividing the N strategies meeting the physical constraint condition into a first group and a second group based on the fitness, wherein the fitness of the strategies of the first group is greater than that of the strategies of the second group, and the fitness indicates the deployment cost and the default cost degree of the strategies; a2: preprocessing the cloud resources of each strategy of the first group to obtain a preprocessed strategy, and determining whether the preprocessed strategy meets the physical constraint condition; a3: if Q strategies do not meet the physical constraint condition, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint condition or not until Q strategies meeting the physical constraint condition are obtained, wherein Q is a positive integer; a4: determining a second group of strategies, Q strategies meeting the physical constraint conditions and the fitness of each strategy in the pretreated strategies meeting the physical constraint conditions;
the determining module is configured to determine, as the deployment policy of the K tasks, a policy with the minimum fitness median of the N fitness values calculated by the executing module W; w is an integer greater than 1.
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